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Five-user quantum virtual local area network with an AlGaAs entangled photon source

Science China(Physics,Mechanics & Astronomy) 2025年第3期68卷 61-73页

作者： Cheng Qian Hongkai Tian Xu Jing Yichen Liu Zhe Chen Huiran Luo Yuan Du Xiaodong Zheng Tangsheng Chen Yuechan Kong Hua-Lei Yin Dong Jiang Bin Niu Liangliang Lu Key Laboratory of State Manipulation and Advanced Materials in Provincial Universities School of Physical Science and TechnologyNanjing Normal UniversityNanjing210023China School of Internet Anhui UniversityHefei230039China National Key Laboratory of Solid-State Microwave Devices and Circuits Nanjing Chip Valley Industrial Technology InstituteNanjing Electronic Devices InstituteNanjing210016China Research Center for Quantum Optics and Quantum Communication School of ScienceQingdao University of TechnologyQingdao266520China Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials and Micro-nano Devices Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)Renmin University of ChinaBeijing100872China Beijing Academy of Quantum Information Sciences Beijing100193China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei230601China National Laboratory of Solid-State Microstructures and School of Physics Nanjing UniversityNanjing210093China Hefei National Laboratory Hefei230088China

The promotion of quantum network applications demands the scalable connection of quantum *** is preferable to set up multiple logical networks coexisting on a single physical network infrastructure to accommodate a larger number of *** we present a quantum virtual network architecture that offers this level of scalability,without being constrained to a fixed physical-layer network relying solely on passive multiplexing *** architecture can be understood as arising from the superposition of a fully connected entanglement distribution network and port-based virtual local area network,which group multiusers by access *** terms of hardware,we leverage a semiconductor chip with a high figure-of-merit modal overlap to directly generate high-quality polarization entanglement,and a streamlined polarization analysis module,which requires only one single-photon detector for each end *** experimentally perform the BBM92 QKD protocol on the five-user quantum virtual network and demonstrate voice and image encryption on a campus area *** results may provide insights into the realization of large-scale quantum networks with integrated and cost-efficient photonic architecture.

关键词： quantum network quantum entanglement distribution integrated photonics image and voice encryption

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to data Clustering

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Computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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AI-Enhanced Secure data Aggregation for Smart Grids with Privacy Preservation

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Computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Exploring Heterogeneity and Uncertainty for Graph-based Cognitive Diagnosis Models in Intelligent Education 25

Exploring Heterogeneity and Uncertainty for Graph-based Cogn...

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Proceedings of the 31st ACM SIGKDD Conference on knowledge Discovery and data Mining V.1

作者： Pengyang Shao Yonghui Yang Chen Gao Lei Chen Kun Zhang Chenyi Zhuang Le Wu Yong Li Meng Wang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei Anhui China BNRist Tsinghua University Beijing China Department of Electronic Engineering BNRist Tsinghua University Beijing China Ant Group HangZhou Zhejiang China

ISBN: (纸本)9798400712456

Graph-based Cognitive Diagnosis (CD) has attracted much research interest due to its strong ability on inferring students' proficiency levels on knowledge concepts. While graph-based CD models have demonstrated remarkable performance, we contend that they still cannot achieve optimal performance due to the neglect of edge heterogeneity and uncertainty. Edges involve both correct and incorrect response logs, indicating heterogeneity. Meanwhile, a response log can have uncertain semantic meanings, e.g., a correct log can indicate true mastery or fortunate guessing, and a wrong log can indicate a lack of understanding or a careless mistake. In this paper, we propose an Informative Semantic-aware Graph-based Cognitive Diagnosis model (ISG-CD), which focuses on how to utilize the heterogeneous graph in CD and minimize effects of uncertain edges. Specifically, to explore heterogeneity, we propose a semantic-aware graph neural networks based CD model. To minimize effects of edge uncertainty, we propose an Informative Edge Differentiation layer from an information bottleneck perspective, which suggests keeping a minimal yet sufficient reliable graph for CD in an unsupervised way. We formulate this process as maximizing mutual information between the reliable graph and response logs, while minimizing mutual information between the reliable graph and the original graph. After that, we prove that mutual information maximization can be theoretically converted to the classic binary cross entropy loss function, while minimizing mutual information can be realized by the Hilbert-Schmidt Independence ***, we adopt an alternating training strategy for optimizing learnable parameters of both the semantic-aware graph neural networks based CD model and the edge differentiation layer. Extensive experiments on three real-world datasets have demonstrated the effectiveness of ISG-CD.

关键词： cognitive diagnosis

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LSDK-LegalSum: improving legal judgment summarization using logical structure and domain knowledge

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Journal of King Saud University - Computer and Information Sciences 2025年第1期37卷 1-15页

作者： Gao, Wei Yu, Shuai Qin, Yongbin Yang, Caiwei Huang, Ruizhang Chen, Yanping Lin, Chuan Engineering Research Center of Text Computing & CognitiveIntelligence Ministry of Education Guizhou University Guizhou Huaxi Guiyang 550025 China State Key Laboratory of Public Big Data Guizhou University Guizhou Huaxi Guiyang 550025 China College of Computer Science and Technology Guizhou University Guizhou Huaxi Guiyang 550025 China

Legal judgment summarization enhances the efficiency and accuracy of processing and retrieving similar cases by distilling key information from legal judgment documents. Legal judgments, which record the trial process and outcome, tend to have lengthy texts. However, most existing methods for legal judgment summarization struggle to effectively handle long texts and focus only on the serialized text information, neglecting the auxiliary role of the document’s logical structure in generating summaries. Additionally, legal judgment documents contain extensive domain knowledge formed by professional legal terminology, which is crucial for accurately understanding and analyzing the content of the documents. We propose a method to improve the quality of legal judgment summaries by utilizing logical structures and domain knowledge (LSDK-LegalSum). Specifically, we first use a multi-label document classification approach to divide the legal judgment into four parts: ‘Type’, ‘Claim’, ‘Fact’ and ‘Result’, then generate a summary of the ‘Type’ part, and combine it with the ‘Claim’ part to create a new summary, and so forth until the final summary is produced. Moreover, the case descriptions in legal judgments cause substantial textual noise;therefore, we employ a text-denoising method that integrates domain knowledge to further enhance the performance of the summaries. Extensive experiments on the CAIL2020 legal summarization dataset have shown that our method achieves improvements of 8.37, 16.64, and 13.07 percentage points in Rouge-1, Rouge-2, and Rouge-L scores respectively over the state-of-the-art models. This demonstrates that our method not only produces concise and semantically complete summaries but also ensures that the content is logically rigorous and well-organized, meeting practical needs more effectively. © The Author(s) 2025.

关键词： Abstractive summarization Domain knowledge Legal judgment summarization Logical structure Low-source summarization

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A Redactable Blockchain-Based Anonymous Announcement Scheme for VANETs

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Yang, Yuxiang Chen, Yuling Liu, Zhiquan Meng, Yan Sang, Haiwei Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Jinan University College of Cyber Security Guangzhou510632 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai2022240 China Guizhou Education University School of Mathematics and Big Data Guiyang550018 China

Blockchain serves as a trust layer for data exchange in Vehicular Ad-hoc Networks (VANETs) due to its immutability and transparency. However, it can also be abused to spread false and inaccurate information. Additionally, the explosive growth of data in VANETs and the limited storage capacity of edge nodes make it challenging to maintain the entire blockchain. To address these challenges, we propose an anonymous vehicle announcement scheme based on redactable blockchain. Specifically, we propose a novel ephemeral trapdoor revocable chameleon hash scheme (ETRCH) that enables decentralized management and enforced revocation of redaction privileges. ETRCH deploys multiple regulators, each capable of creating multiple ephemeral trapdoors. These regulators enable k-out-of-n edge nodes associated with the same ephemeral trapdoor to collaboratively rewrite blockchain data, thereby addressing storage limitations. In addition, k-out-of-n regulators can cooperate to update the ephemeral trapdoor and revoke the redaction privileges of any malicious regulator discovered to be abusing their privileges. To facilitate the threshold authentication of announcement messages, we construct a redactable threshold ring signature scheme (RTRS) based on ETRCH, enabling anonymous signing of announcements in VANETs. Additionally, if a regulator detects a false or misleading message, the content can be promptly rewritten. Finally, we conducted rigorous security analysis and comprehensive experiments to evaluate the performance of the proposed scheme. The results demonstrate that compared to VANETs systems based on immutable blockchains, our scheme is both secure and efficient. © 2000-2011 IEEE.

关键词： Vehicular ad hoc networks

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A Lightweight Aerial Image Object Detector Based on Mask Information Enhancement

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zhou, Liming Zhao, Shuai Liu, Zhehao Zhang, Wanjun Qiao, Baojun Liu, Yang Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan Kaifeng China Henan University School of Computer and Information Engineering Henan Kaifeng China

Object detection in UAV imagery is highly valuable in both military and civilian domains. However, deploying conventional detectors is challenging due to limited computing resources on UAV platforms. Additionally, UAV images often contain densely arranged objects, demanding higher accuracy from detectors. In response to these challenges, we proposed the lightweight aerial object detection method based on mask feature enhancement, named BFDet. First, to enhance the feature extraction capability of the model under limited parameters, we designed the Balanced Channel Attention Layer (BCA Layer). The BCA Layer improves the extraction of high-quality channel information during feature processing. Second, to enhance model performance while minimizing parameters, we designed a feature extraction network utilizing the Efficient Feature Extraction Module (EFEM) and the Downsampling Module (DM) as its backbone. This design reduces the number of parameters while improving the extraction of discriminative features. Third, to enrich the semantic information of features, we proposed a Progressive Space Pyramid Pooling (PSPP) module, obtaining features with rich semantic information to enhance model performance. Finally, to tackle the challenge of detecting densely packed objects, we proposed a Mask Information Enhancement Module (MIEM) to obtain the fine-grained features, enhancing the model's discriminative ability in dense scenes. The proposed BFDet underwent extensive experimentation on both the Vis-Drone and UAVDT datasets. The mAP0.5 and FPS values of BFDet reach 51.4% and 33 respectively on the VisDrone dataset, and the parameter number is only 5.6M. © 1963-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A universal approach to high-performance thermoelectric module design for power generation (vol 9, 101818, 2025)

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JOULE 2025年第4期9卷

作者： Cheng, Jinxuan Xue, Wenhua Zhang, Tianyu Li, Xiaofang Zhu, Yichen Yin, Li Yao, Honghao Fu, Zixuan Wu, Longzhi Chen, Chen Zhao, Peng Ma, Xiaojing Jiang, Feng Wang, Xiaodong Li, Mingyu Mao, Jun Wang, Yumei Cao, Feng Zhang, Qian School of Materials Science and Engineering and Institute of Materials Genome & Big Data Harbin Institute of Technology Shenzhen 518055 China Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Science Beijing 100190 China School of Science Harbin Institute of Technology Shenzhen 518055 China Sauvage Laboratory for Smart Materials School of Materials Science and Engineering Harbin Institute of Technology Shenzhen 518055 China State Key Laboratory of Advanced Welding and Joining Harbin Institute of Technology Harbin 150001 China School of Physical Sciences Great Bay University Dongguan 523000 China Institute of Special Environments Physical Sciences Harbin Institute of Technology Shenzhen 518055 China Beijing Branch of Songshan Lake Materials Laboratory Dongguan 523808 China

Thermoelectric modules fabricated by traditional welding fall short of achieving optimal conversion efficiencies, primarily due to performance degradation of materials at high temperatures, severe elemental diffusion, and residual thermal stress at the interface. Transient liquid phase bonding enables the realization of joints with high-melting-point compounds at low bonding temperatures, providing a promising solution for achieving "low-temperature bonding and high-temperature service." Owing to the low eutectic point of the solder and high melting points of compounds, we optimize the design of a germanium-telluride-based module at 533 K, which is successfully applied at the hot-side temperature of 773 K. Attributed to high-performance materials and reliable joints, the module realizes a high conversion efficiency of ∼15.1% and remains stable throughout 150 h of service. By adopting the same approach, the low- (Bi2Te3) and high-temperature (half-Heusler) modules are assembled. We provide a promising and general route for the assembly of full-temperature-range thermoelectric devices.

关键词： transient liquid phase bonding thermoelectric module intermetallic compounds GeTe

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A knowledge-Enhanced Contrastive Learning Network for Weakly-Supervised Aspect Detection

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 506-517页

作者： Zhuoming Zheng Yi Cai Liuwu Li School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education South China University of Technology Guangzhou China

Aspect detection, aiming at identifying the aspects of review segments, is a fundamental task in opinion mining and aspect-based sentiment analysis. Due to the high cost and time consuming of human-annotation for massive reviews, several unsupervised and weakly-supervised methods are proposed recently. However, existing weakly-supervised models are mostly seed-driven methods based on co-occurrence of words, which suffer from lacking the ability of detecting the aspects with infrequent aspect terms and identifying Misc aspect. To tackle these problems, we leverage external knowledge to enhance the representation of aspects and segments by a weakly-supervised method. In this paper, we propose an aspect knowledge-enhanced contrastive learning (AKECL) network with two powerful knowledge-enhanced encoders for aspects and reivew segments to enhance weakly-supervised aspect detection task. Experiments in seven different domains show that AKECL outperforms the competitive baselines, and demonstrate the effectiveness of our proposed method, as well as the improvement by introducing external knowledge.

关键词： Reviews Contrastive learning Vectors Feature extraction knowledge graphs Speech processing Sentiment analysis knowledge engineering Hardware Touch sensitive screens

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Early Warning Thresholds for Microalgal Blooms and Environmental Drivers of Substrate Microalgae Recovery in Litopenaeus vannamei Aquaculture

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Water, Air, and Soil Pollution 2025年第6期236卷 1-16页

作者： Ma, Weixing Shi, Yuanyuan Ma, Ni Liu, Gaojian Gao, Yuxin Ma, Jiaru Yi, Zhaohui Yang, Min Li, Zhaoxia College of Environmental Science & Engineering Key Laboratory for Advanced Technology in Environmental Protection of Jiangsu Province Yancheng Institute of Technology Jiangsu Province Yancheng224051 China School of Marine and Biological Engineering Yancheng Institute of Technology Jiangsu Province Yancheng224051 China Jiangsu YIDA TestingTechnology Co. Ltd. Building A- 15 Big Data Industrial Park Chengnan New District Jiangsu Province Yancheng224051 China Shenzhen518055 China

In response to the global demand for high-quality protein, Litopenaeus vannamei aquaculture has rapidly expanded. However, microalgal blooms often degrade water quality, resulting in reduced yields and significant financial losses. This study investigated the recovery dynamics of substrate microalgae, which act as seed banks for microalgal blooms, and identified early warning thresholds for algae under varying environmental conditions. Sediments from bfIGuccess (BS) and breeding failure (BF) sheds were analyzed to evaluate the effects of temperature (6 °C–21 °C), light intensity (50–2,000 lx), and nutrient types (BG11 and raw water). The findings indicated that environmental factors influencing microalgal recovery followed the priority order: temperature > light > nutrient salts. The highest recovery was observed at temperatures of 11 °C and 16 °C under 2,000 lx illumination. key early warning algal species, including Melosira granulata, Cyclotella, and Chlorella, exhibited thresholds of 0.9–42.9 × 104 cells/g, 0.1–28.8 × 104 cells/g, and 1.0–31.7 × 104 cells/g, respectively. This study uniquely linked substrate algae recovery to aquaculture failures, providing quantitative thresholds and actionable insights for proactive microalgal blooms monitoring and control. These findings form a scientific basis for improving water quality management and ensuring sustainable L. vannamei aquaculture © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Aquaculture

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